Earticle

초록

영어

Querying a database is a common task for most database systems. To query a database is to find some answers from stored data. Traditional database systems return exactly what is being asked. This is a method of direct query answering and users are required to construct a query intelligently and properly. To remove the burden of intelligence from the database users, the concept of intelligent or cooperative query answering has emerged. The process of intelligent query answering consists of analyzing the intent of query, rewriting the query based on the intention and other kinds of knowledge, and providing answers in an intelligent way. Intelligent answers could be generalized, neighborhood or associated information relevant to the query. This concept is based on the assumption that some users might not have a clear idea of the database content and schema. Therefore, it is difficult to pose queries correctly to get some useful answers. Producing answers effectively depends largely on users' knowledge about the query language and the database schema. Knowledge, either intentional or extensional, is the key ingredient of intelligence. In order to improve effectiveness and convenience of querying databases, we design a systematic way to analyze user's request and revise the query with data mining models and materialized views. The models obtained from the automatic knowledge extraction process is a set of association rules discovered from the database contents. Materialized views are pre-computed and normally aggregated data from base tables to speed up the processing of frequently asked queries. This paper presents the knowledge acquisition method focusing on association pattern mining, its implementation, and a systematic method of rewriting query with association patterns and materialized views. We perform preliminary efficiency tests of the proposed system. The experimental results demonstrate the effectiveness of our system in answering queries sharing the same pattern as the available knowledge and the pre-computed views.

목차

Abstract
 1. Introduction
 2. Related Work
 3. The Design and Implementation of Semantic-based Query Optimization
 4. Experimentation and Query Answering Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Nittaya Kerdprasop [ Data Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology ]
• Kittisak Kerdprasop [ Data Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605